Unsupervised Video Domain Adaptation. Despite the great progress in image-based UDA, only a few methods have recently attempted video-based UDA. In [1]}, a temporal attentive adversarial adaptation network (TA\(^3\) N) is proposed to integrate a temporal relation module for temporal alignment. [2]} proposed a SAVA method using self-supervised clip order prediction and clip attention-based alignment.
Based on a cross-domain co-attention mechanism, the temporal co-attention network TCoN [3]} focuses on common key frames across domains for better alignment. [4]} pay more attention to the domain-agnostic classifier by using a network topology of the bipartite graph to model the cross-domain correlations. Instead of using adversarial learning, [5]} develop an end-to-end temporal contrastive learning framework named CoMix with background mixing and target pseudo-labels. Recently, [6]} learn multiple domain discriminators for multi-level temporal attentive features to achieve better alignment, while [7]} exploit two-headed deep architecture to learn a more robust target classifier by the combination of cross-entropy and contrastive losses.
Although these methods have advanced video-based UDA, they align features with diverse information mixed up from a compression perspective, which leaves room for further improvement.
